[jira] [Created] (ARROW-17154) [C++] Change cmake project name from arrow_python to pyarrow_cpp
Alenka Frim created ARROW-17154: --- Summary: [C++] Change cmake project name from arrow_python to pyarrow_cpp Key: ARROW-17154 URL: https://issues.apache.org/jira/browse/ARROW-17154 Project: Apache Arrow Issue Type: Sub-task Components: C++ Reporter: Alenka Frim Assignee: Alenka Frim Fix For: 10.0.0 See discussion https://github.com/apache/arrow/pull/13311#discussion_r926198302 -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17153) [CI][Homebrew] Require glib-utils
Kouhei Sutou created ARROW-17153: Summary: [CI][Homebrew] Require glib-utils Key: ARROW-17153 URL: https://issues.apache.org/jira/browse/ARROW-17153 Project: Apache Arrow Issue Type: Improvement Components: Continuous Integration, GLib Reporter: Kouhei Sutou Assignee: Kouhei Sutou Fix For: 9.0.0 -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17152) [Docs] Enable dark mode on documentation site
Will Jones created ARROW-17152: -- Summary: [Docs] Enable dark mode on documentation site Key: ARROW-17152 URL: https://issues.apache.org/jira/browse/ARROW-17152 Project: Apache Arrow Issue Type: New Feature Reporter: Will Jones Fix For: 10.0.0 Attachments: Screen Shot 2022-07-20 at 3.10.51 PM.png, Screen Shot 2022-07-20 at 3.12.18 PM.png pydata-sphinx-theme adds dark mode in version 0.9.0. We will need to adapt our logo ([see docs|https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/configuring.html?highlight=dark#different-logos-for-light-and-dark-mode]). There are also some places in the docs where we may need to adjust additional CSS. See attached screenshot. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17151) [Docs] Pin pydata-sphinx-theme to 0.8 to avoid dark mode
Will Jones created ARROW-17151: -- Summary: [Docs] Pin pydata-sphinx-theme to 0.8 to avoid dark mode Key: ARROW-17151 URL: https://issues.apache.org/jira/browse/ARROW-17151 Project: Apache Arrow Issue Type: Improvement Components: Documentation Reporter: Will Jones Fix For: 9.0.0 pydata-sphinx-theme introduced automatic dark mode. However there is a series of changes we need to do (such as providing a dark-mode Arrow logo) before we will be ready for this. For the 9.0.0 release, we should instead pin to the version of pydata-sphinx-theme just before that release. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17150) [R] Allow statically linked libcurl in GCS when building libarrow DLL in RTools
Will Jones created ARROW-17150: -- Summary: [R] Allow statically linked libcurl in GCS when building libarrow DLL in RTools Key: ARROW-17150 URL: https://issues.apache.org/jira/browse/ARROW-17150 Project: Apache Arrow Issue Type: Improvement Components: R Affects Versions: 9.0.0 Reporter: Will Jones Fix For: 10.0.0 Neal's patch in ARROW-16510 enabled libcurl to be linked statically in the google cloud storage dependency, but this only seems to work for static libraries on RTools (Windows). For development Rtools environments, we currently use dynamic Arrow libraries instead, but currently we get linking errors to libcurl when ARROW_GCS is on. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17149) [R] Enable GCS tests for Windows
Will Jones created ARROW-17149: -- Summary: [R] Enable GCS tests for Windows Key: ARROW-17149 URL: https://issues.apache.org/jira/browse/ARROW-17149 Project: Apache Arrow Issue Type: Improvement Components: Continuous Integration, R Affects Versions: 9.0.0 Reporter: Will Jones Fix For: 10.0.0 In ARROW-16879, I found the GCS tests were hanging in CI, but couldn't diagnose why. We should solve that and enable the tests. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[GitHub] [arrow-adbc] lidavidm merged pull request #41: [Python] Complete minimal bindings for ADBC
lidavidm merged PR #41: URL: https://github.com/apache/arrow-adbc/pull/41 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@arrow.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [arrow-adbc] lidavidm closed issue #37: Reorganize and complete Python bindings
lidavidm closed issue #37: Reorganize and complete Python bindings URL: https://github.com/apache/arrow-adbc/issues/37 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@arrow.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[jira] [Created] (ARROW-17148) [R] Improve evaluation of R functions from C++
Dewey Dunnington created ARROW-17148:
Summary: [R] Improve evaluation of R functions from C++
Key: ARROW-17148
URL: https://issues.apache.org/jira/browse/ARROW-17148
Project: Apache Arrow
Issue Type: Improvement
Components: R
Reporter: Dewey Dunnington
There are currently a few places where we call R code from C++ (and after
ARROW-16444 and ARROW-16703 we will have some more where the overhead of
calling into R might be greater than the time it takes to actually evaluate the
function/the functions will be called in a tight loop).
The current approach uses {{cpp11::function}}. This is totally fine and safe
but generates some ugly backtraces on error and is potentially slower than the
lean-and-mean approach of purrr (whose entire job is to call R functions in a
loop and has been heavily optimized). The purrr approach is to construct the
{{call()}} and calling environment in advance and then just run `Rf_eval(call,
env)` in the loop. This is both faster (fewer R API calls) and generates better
backtraces (e.g., {{Error in fun(arg1, arg2)}} instead of {{Error in
(function(a, b) { ...the whole content of the function ... })(every, deparsed,
argument)}}.
Before optimizing that heavily we should of course benchmark to see exactly how
much that matters!
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[jira] [Created] (ARROW-17147) [R] parse_date_time should support locale parameter
Rok Mihevc created ARROW-17147: -- Summary: [R] parse_date_time should support locale parameter Key: ARROW-17147 URL: https://issues.apache.org/jira/browse/ARROW-17147 Project: Apache Arrow Issue Type: Improvement Components: R Reporter: Rok Mihevc See [discussion here|https://github.com/apache/arrow/pull/13627#discussion_r924875872]. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17146) [R] parse_date_time should support quiet = FALSE
Rok Mihevc created ARROW-17146: -- Summary: [R] parse_date_time should support quiet = FALSE Key: ARROW-17146 URL: https://issues.apache.org/jira/browse/ARROW-17146 Project: Apache Arrow Issue Type: Improvement Components: R Reporter: Rok Mihevc See [discussion here|https://github.com/apache/arrow/pull/13627#discussion_r924875872]. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[GitHub] [arrow-adbc] lidavidm opened a new pull request, #41: [Python] Complete minimal bindings for ADBC
lidavidm opened a new pull request, #41: URL: https://github.com/apache/arrow-adbc/pull/41 Also refactors the bindings to not depend on PyArrow. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@arrow.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[jira] [Created] (ARROW-17145) [C++] Compilation warnings on gcc in release mode
Antoine Pitrou created ARROW-17145:
--
Summary: [C++] Compilation warnings on gcc in release mode
Key: ARROW-17145
URL: https://issues.apache.org/jira/browse/ARROW-17145
Project: Apache Arrow
Issue Type: Bug
Components: C++
Reporter: Antoine Pitrou
With gcc 10.3 I get this warning in release mode.
{code}
[168/321] Building CXX object
src/arrow/CMakeFiles/arrow_testing_objlib.dir/compute/exec/test_util.cc.o
In file included from
/home/antoine/arrow/dev/cpp/src/arrow/compute/exec/test_util.h:28,
from
/home/antoine/arrow/dev/cpp/src/arrow/compute/exec/test_util.cc:18:
/home/antoine/arrow/dev/cpp/src/arrow/compute/exec.h: In member function 'R
arrow::internal::FnOnce::FnImpl::invoke(A&& ...) [with Fn =
arrow::Future<>::WrapResultyOnComplete::Callback::ThenOnComplete
>)::,
arrow::Future<>::PassthruOnFailure
>):: > > >; R = void; A = {const arrow::FutureImpl&}]':
/home/antoine/arrow/dev/cpp/src/arrow/compute/exec.h:177:21: warning:
'*((void*)(&)+8).arrow::compute::ExecBatch::length' may be used
uninitialized in this function [-Wmaybe-uninitialized]
177 | struct ARROW_EXPORT ExecBatch {
| ^
{code}
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[jira] [Created] (ARROW-17144) Adding sqrt Function
Sahaj Gupta created ARROW-17144: --- Summary: Adding sqrt Function Key: ARROW-17144 URL: https://issues.apache.org/jira/browse/ARROW-17144 Project: Apache Arrow Issue Type: New Feature Reporter: Sahaj Gupta Adding Sqrt Function. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17143) [R] Add examples working with `tidyr::unnest`and `tidyr::unnest_longer`
SHIMA Tatsuya created ARROW-17143:
-
Summary: [R] Add examples working with `tidyr::unnest`and
`tidyr::unnest_longer`
Key: ARROW-17143
URL: https://issues.apache.org/jira/browse/ARROW-17143
Project: Apache Arrow
Issue Type: Improvement
Components: Documentation, R
Affects Versions: 8.0.1
Reporter: SHIMA Tatsuya
Related to ARROW-8813
The arrow package can convert json files to data frames very easily, but
{{tidyr::unnest_longer}} is needed for array expansion.
Wonder if {{tidyr}} could be added to the recommended package and examples like
this could be included in the documentation and test cases.
{code:r}
tf <- tempfile()
on.exit(unlink(tf))
writeLines('
{ "hello": 3.5, "world": false, "foo": { "bar": [ 1, 2 ] } }
{ "hello": 3.25, "world": null }
{ "hello": 0.0, "world": true, "foo": { "bar": [ 3, 4, 5 ] } }
', tf)
arrow::read_json_arrow(tf) |>
tidyr::unnest(foo, names_sep = ".") |>
tidyr::unnest_longer(foo.bar)
#> # A tibble: 6 × 3
#> hello world foo.bar
#>
#> 1 3.5 FALSE 1
#> 2 3.5 FALSE 2
#> 3 3.25 NA NA
#> 4 0TRUE3
#> 5 0TRUE4
#> 6 0TRUE5
{code}
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[jira] [Created] (ARROW-17142) `equals` method on Parquet Metadata segfaults when passed `None
Kshiteej K created ARROW-17142:
--
Summary: `equals` method on Parquet Metadata segfaults when passed
`None
Key: ARROW-17142
URL: https://issues.apache.org/jira/browse/ARROW-17142
Project: Apache Arrow
Issue Type: Bug
Reporter: Kshiteej K
{code:java}
import pyarrow as pa import pyarrow.parquet as pq
table = pa.table({"a": [1, 2, 3]})
# Here metadata is None
metadata = table.schema.metadata
fname = "data.parquet"
pq.write_table(table, fname) # Get `metadata`.
r_metadata = pq.read_metadata(fname)
# Equals on Metadata segfaults when passed None
r_metadata.equals(metadata) {code}
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[jira] [Created] (ARROW-17141) [C++] Enable selecting nested fields in StructArray with field path
Rok Mihevc created ARROW-17141: -- Summary: [C++] Enable selecting nested fields in StructArray with field path Key: ARROW-17141 URL: https://issues.apache.org/jira/browse/ARROW-17141 Project: Apache Arrow Issue Type: Improvement Components: C++ Reporter: Rok Mihevc Currently selecting a nested field in a StructArray requires multiple selects or flattening of schema. It would be more user friendly to provide a field path e.g.: field_in_top_struct.field_in_substruct. -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17140) Adding Floor Function
Sahaj Gupta created ARROW-17140: --- Summary: Adding Floor Function Key: ARROW-17140 URL: https://issues.apache.org/jira/browse/ARROW-17140 Project: Apache Arrow Issue Type: New Feature Reporter: Sahaj Gupta Adding Floor Function -- This message was sent by Atlassian Jira (v8.20.10#820010)
[jira] [Created] (ARROW-17139) [Python] Add field() method to get field from StructType
Joris Van den Bossche created ARROW-17139:
-
Summary: [Python] Add field() method to get field from StructType
Key: ARROW-17139
URL: https://issues.apache.org/jira/browse/ARROW-17139
Project: Apache Arrow
Issue Type: Improvement
Components: Python
Reporter: Joris Van den Bossche
>From ARROW-17047:
We could also add a {{field()}} method to {{StructType}} that returns you a
field? (that is more discoverable than [], and would be consistent with a
Schema and with StructArray (to get the child array for that field))
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[jira] [Created] (ARROW-17137) [Python] Converting data frame to Table with large nested column fails `Invalid Struct child array has length smaller than expected`
Simon Weiß created ARROW-17137:
--
Summary: [Python] Converting data frame to Table with large nested
column fails `Invalid Struct child array has length smaller than expected`
Key: ARROW-17137
URL: https://issues.apache.org/jira/browse/ARROW-17137
Project: Apache Arrow
Issue Type: Bug
Components: Python
Reporter: Simon Weiß
Hey,
I have a data frame for which one column is a nested struct array. Converting
it to a `pyarrow.Table` fails if the data frame gets too big. I could reproduce
the bug with a minimal example with anonymized data that is roughly similar to
mine. When I set, e.g., `N_ROWS=500_000`, or smaller, it is working fine.
```python
import pandas as pd
import pyarrow as pa
N_ROWS = 800_000
item_record = {
"someImportantAssets": [
{
"square":
"https://some.super.loong.link.com/withmany/lorem/upload/ipsum/stilllooonger/lorem/\{someparameter}/156fdjjf644984dfdfaera648/specificLink-i15348891;
}
],
"id": "i15348891",
"title": "Some Long Item Title i15348891",
}
user_record = {
"userId": "faa4648-4964drf-64648fafa648-4648falj",
"data": [item_record for _ in range(24)],
}
df = pd.DataFrame([user_record for _ in range(N_ROWS)])
table = pa.Table.from_pandas(df)
```
```python-traceback
Traceback (most recent call last):
table = pa.Table.from_pandas(df)
File "pyarrow/table.pxi", line 1658, in pyarrow.lib.Table.from_pandas
File "pyarrow/table.pxi", line 1702, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 1314, in pyarrow.lib.Table.validate
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array
invalid: Invalid: Struct child array #1 invalid: Invalid: List child array
invalid: Invalid: Struct child array #0 has length smaller than expected for
struct array (13256071 < 13256072)
```
The length is always smaller than expected by 1.
h2. Expected behavior:
Run without errors or fail with a better error message.
h2. System Info and Versions:
Apple M1 Pro but also happened on amd64 Linux machine on AWS
```
arrow-cpp 7.0.0 py39h8a997f0_8_cpu conda-forge
pyarrow 7.0.0 py39h3a11367_8_cpu conda-forge
python 3.9.7 h54d631c_3_cpython conda-forge
```
I could also reproduce with `pyarrow 8.0.0`
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[jira] [Created] (ARROW-17138) [Python] Converting data frame to Table with large nested column fails `Invalid Struct child array has length smaller than expected`
Simon Weiß created ARROW-17138:
--
Summary: [Python] Converting data frame to Table with large nested
column fails `Invalid Struct child array has length smaller than expected`
Key: ARROW-17138
URL: https://issues.apache.org/jira/browse/ARROW-17138
Project: Apache Arrow
Issue Type: Bug
Components: Python
Reporter: Simon Weiß
Hey,
I have a data frame for which one column is a nested struct array. Converting
it to a `pyarrow.Table` fails if the data frame gets too big. I could reproduce
the bug with a minimal example with anonymized data that is roughly similar to
mine. When I set, e.g., `N_ROWS=500_000`, or smaller, it is working fine.
```python
import pandas as pd
import pyarrow as pa
N_ROWS = 800_000
item_record = {
"someImportantAssets": [
{
"square":
"https://some.super.loong.link.com/withmany/lorem/upload/ipsum/stilllooonger/lorem/\{someparameter}/156fdjjf644984dfdfaera648/specificLink-i15348891;
}
],
"id": "i15348891",
"title": "Some Long Item Title i15348891",
}
user_record = {
"userId": "faa4648-4964drf-64648fafa648-4648falj",
"data": [item_record for _ in range(24)],
}
df = pd.DataFrame([user_record for _ in range(N_ROWS)])
table = pa.Table.from_pandas(df)
```
```python-traceback
Traceback (most recent call last):
table = pa.Table.from_pandas(df)
File "pyarrow/table.pxi", line 1658, in pyarrow.lib.Table.from_pandas
File "pyarrow/table.pxi", line 1702, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 1314, in pyarrow.lib.Table.validate
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array
invalid: Invalid: Struct child array #1 invalid: Invalid: List child array
invalid: Invalid: Struct child array #0 has length smaller than expected for
struct array (13256071 < 13256072)
```
The length is always smaller than expected by 1.
h2. Expected behavior:
Run without errors or fail with a better error message.
h2. System Info and Versions:
Apple M1 Pro but also happened on amd64 Linux machine on AWS
```
arrow-cpp 7.0.0 py39h8a997f0_8_cpu conda-forge
pyarrow 7.0.0 py39h3a11367_8_cpu conda-forge
python 3.9.7 h54d631c_3_cpython conda-forge
```
I could also reproduce with `pyarrow 8.0.0`
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[jira] [Created] (ARROW-17136) open_append_stream throwing an error if file does not exists
Sagar Shinde created ARROW-17136: Summary: open_append_stream throwing an error if file does not exists Key: ARROW-17136 URL: https://issues.apache.org/jira/browse/ARROW-17136 Project: Apache Arrow Issue Type: Bug Components: Python Affects Versions: 8.0.0 Reporter: Sagar Shinde as per the document method, open_append_stream will create the file if does not exists. But when I try to append to the file in hdfs it is throwing an error like file, not found. hdfsOpenFile(/tmp/xyz.json): FileSystem#append((Lorg/apache/hadoop/fs/Path;)Lorg/apache/hadoop/fs/FSDataOutputStream;) error: RemoteException: Failed to append to non-existent file /tmp/xyz.json for client 10.128.8.11 at org.apache.hadoop.hdfs.server.namenode.FSDirAppendOp.appendFile(FSDirAppendOp.java:104) at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.appendFile(FSNamesystem.java:2639) at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.append(NameNodeRpcServer.java:805) at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.append(ClientNamenodeProtocolServerSideTranslatorPB.java:487) at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java) at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:524) at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1025) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:876) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:822) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1730) at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2682) java.io.FileNotFoundException: Failed to append to non-existent file /tmp/xyz.json for client 10.128.8.11 at org.apache.hadoop.hdfs.server.namenode.FSDirAppendOp.appendFile(FSDirAppendOp.java:104) at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.appendFile(FSNamesystem.java:2639) at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.append(NameNodeRpcServer.java:805) at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.append(ClientNamenodeProtocolServerSideTranslatorPB.java:487) at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java) at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:524) at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1025) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:876) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:822) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1730) at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2682) at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) at java.lang.reflect.Constructor.newInstance(Constructor.java:423) at org.apache.hadoop.ipc.RemoteException.instantiateException(RemoteException.java:121) at org.apache.hadoop.ipc.RemoteException.unwrapRemoteException(RemoteException.java:88) at org.apache.hadoop.hdfs.DFSClient.callAppend(DFSClient.java:1367) at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:1424) at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:1394) at org.apache.hadoop.hdfs.DistributedFileSystem$5.doCall(DistributedFileSystem.java:423) at org.apache.hadoop.hdfs.DistributedFileSystem$5.doCall(DistributedFileSystem.java:419) at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81) at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:431) at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:400) at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:1386) Caused by: org.apache.hadoop.ipc.RemoteException(java.io.FileNotFoundException): Failed to append to non-existent file /tmp/xyz.json for client 10.128.8.11 at org.apache.hadoop.hdfs.server.namenode.FSDirAppendOp.appendFile(FSDirAppendOp.java:104) at
